54 results about "Process quality" patented technology

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention relates to a multi-modal data fusion 3C assembly process quality accurate prediction method, and belongs to the field of 3C intelligent manufacturing, and the method comprises the steps: carrying out the multi-modal data fusion, and forming a unified feature space containing process parameters and quality characterization; performing multi-dimensional analysis on process quality, performing hierarchical analysis on structured process parameters and unstructured images, and establishing cross-modal feature mapping; predicting the process quality, evaluating the contribution degree of process parameters to the quality by utilizing an integrated learning model based on a fused feature set, screening key parameters to reduce the complexity of the model, dynamically adjusting the prediction model, designing a'sliding window + multi-dimensional index 'monitoring system, and evaluating the model performance and the data distribution drift in real time. Establishing a time-performance-distribution three-dimensional trigger mechanism, and starting an adjustment strategy according to different priorities; a'cleaning-complementation-mechanism-mapping-self-adaption 'closed loop is realized, and the timeliness and accuracy of 3C assembly quality prediction are remarkably improved.

The invention discloses a product assembly abnormal process parameter tracing method and device, and relates to the technical field of abnormal parameter tracing, the method takes a knowledge graph as a core technical means, and adopts a knowledge-driven ammunition assembly process quality control model to guide ammunition assembly process quality control. According to the method, the Bayesian network model and the knowledge traceability strategy are fused to help deeply identify and understand the root cause of the assembly quality problem, and powerful support is provided for systematic solution of the problem. The method can comprehensively evaluate the influence capability of process technological parameters on quality parameters, intelligently evaluate the quality defect category and predict the assembly quality consistency, and realizes the quality control of the ammunition assembly process.

This invention relates to an intermediate compound of MK-8876 and its preparation method, providing a highly efficient synthetic method for preparing MK-8876. It boasts advantages such as high efficiency, low cost, stable and safe process, controllable process quality, and the potential for industrial-scale production, thus holding significant potential in this field.

The invention provides a window manufacturing apparatus and a window manufacturing method using the same. The window manufacturing apparatus may include: a melting unit configured to supply molten glass; and a shape processing unit disposed adjacent to the melting unit in a direction in which the molten glass is discharged from the melting unit. The shape processing unit may include a protruding portion that forms a groove in the preliminary glass discharged from the melting unit, so that glass manufacturing and shape processing can be continuously performed, thereby improving process quality of the manufactured window and improving process economy.

The application discloses an industrial full-link through implementation method based on unique identification and double contract anchoring, and belongs to the technical field of intelligent manufacturing informatization. The method takes a plan number as a unique and unchangeable identification throughout the full life cycle from raw material warehousing to finished product warehousing, takes a contract hash value as a cross-table association anchor point, adopts a step-by-step anchoring mechanism of sales contract priority binding and procurement contract subsequent binding, and combines batch processing, transaction safety control and full-chain backtracking synchronization technology to realize bidirectional synchronous updating of procurement and sales contract information in full life cycle data tables such as raw material inventory tables and production process quality inspection record tables. The application breaks the strong coupling of the procurement and production processes, supports cost information post-recording, solves the pain points of data disconnection, untraceability, low efficiency and maintenance difficulty of traditional industrial software, has a simple architecture, high performance and stable operation, and is suitable for various intelligent manufacturing systems such as MES and ERP. The application has been applied in actual industrial scenes and has industrialization popularization capability.

The invention discloses a PCB manufacturing process quality prediction and control method based on multi-modal data. The method comprises the following steps: collecting the multi-modal data in a key process for preprocessing; constructing a process semantic chain and carrying out feature coding to obtain a process semantic chain feature sequence; cross-modal attention fusion is carried out in the processes, coupling between the processes is realized through bidirectional cross-process attention and a process residual path, and process contribution degree decomposition is carried out to obtain cross-process joint features; executing quality risk prediction under a shared coding structure through a defect risk probability prediction model; and a technological parameter adjustment scheme is generated and issued for execution, execution feedback data and a quality detection result are collected, and online feedback and model self-correction are completed. The multi-mode cross-process fusion and defect risk probability prediction method is utilized to realize prospective evaluation and adaptive process control of the PCB process quality, and the method has the advantages of accurate prediction, sufficient explanation and intelligent regulation and control.

The invention relates to the technical field of intelligent manufacturing and process quality, in particular to an artware persimmon paint spraying production management system based on digital twinning, which comprises the following steps: constructing an artware multi-dimensional dynamic virtual model through a data acquisition module, and forming a plurality of digital twinning daughters capable of being independently calibrated after the processing of a sub-model decoupling analysis module. And the simulation evolution module enables the daughters to carry out parallel interactive evolution in a unified platform so as to identify a key process fluctuation set influencing the quality. The strategy search module generates candidate regulation and control schemes in parallel for the fluctuation set, and the scheme verification module performs matching and deviation evaluation by using a historical pattern library. And the decision optimization module integrates the evaluation results, iteratively optimizes in a decision space, and finally outputs an equipment control parameter set. According to the method, decoupling accurate mapping of the process links and reverse active discovery of complex quality problems are realized, and the quality control capability and the intelligent level of artware spraying production are improved.

The application discloses a kind of for propanol rectification whole-process quality management and risk early warning method, it is related to propanol rectification quality risk management technical field, comprising: the mapping of establishing working condition time period and product tank section;The deviation sequence of tower top pressure, key plate temperature and product water content is generated;Main correlation degree and lag ring intensity are calculated;Based on the normalized temperature fluctuation amplitude, construct temperature control correction term, calculate time period risk index;Risk is collected to product tank section, combined with flow and value coefficient to calculate work order priority, generate adjustment queue.The application can effectively identify the water content lag drift caused by pressure fluctuation, reduce management delay and disposal cost.

The invention relates to the technical field of temperature control, in particular to a temperature control system and method used in the production process of foamed silica gel, the system can estimate process abnormity in the production process by analyzing images and the temperature in a furnace in the production process in real time, and the temperature of the foamed silica gel can be accurately controlled in combination with future use scenes. The system can predict the final service life of the product in advance and actively give out early warning when the predicted service life does not reach the standard, so that a producer can take intervention measures at the early stage of problem occurrence or before the product leaves the factory, the rejection rate is remarkably reduced, active and accurate quality control is realized, and the product quality is improved. According to the method, production process quality data and future use condition data are combined to construct a comprehensive prediction model, so that a prediction result not only considers the production quality of a product, but also combines a service environment, adopts a gradient lifting decision tree model, and can effectively capture a complex nonlinear relationship among mixed type features.

The application provides a full-process quality control system for automobile molds based on hierarchical quality gates, and relates to the technical field of automobile molds.The system realizes phased, quantifiable and closed-loop quality control of the whole mold manufacturing process by constructing multiple progressive quality gates.The system sets up quality gates with data acquisition, intelligent judgment and feedback control functions at each key stage of assembly, initial adjustment, final adjustment and acceptance, and can automatically evaluate the current quality state at the end of each stage.The first stage ensures that the structure assembly meets the design requirements, the second stage controls the process release according to the precision compliance rate and problem closing rate, the third stage emphasizes the continuous improvement of precision and the complete closed loop of problems, and the fourth stage verifies whether the final product meets the standard of the host factory from the aspects of forming performance and appearance quality.This hierarchical and progressive control mechanism effectively realizes early detection, early interception and early rectification of quality problems, and improves the controllability and consistency of the mold manufacturing process.

The invention relates to the technical field of convolutional neural networks, in particular to a technological process and quality control method and system for a green insulating gas inflation cabinet, and the method comprises the following steps: obtaining a gas injection sequence and valve opening and closing time, associating a gas injection behavior with an operation time point, extracting an assembly process and a stress path, analyzing the boundary change of a spraying image, and determining the quality of the spraying image. And matching a voltage response position, connecting full-flow serial number information, and outputting a quality control matching evaluation table. According to the method, the time mapping relation between the gas injection process and the valve action is constructed, the injection number and the operation intervention node are associated, the offset path is extracted by combining the stress change and displacement data in the sealing structure assembly, and the spraying abnormal area is marked by overlapping the image boundary gray scale and color difference characteristics. And a voltage response position and an image coordinate are linked to position a behavior change area, serial number cross information in the processes of gas injection, assembly, spraying and detection is cut through, an operation path chain and a data comparison channel are formed, and structure behavior tracking and process quality process comparison are supported.

The present application relates to a kind of steel pipe whole-process traceability and intelligent scheduling lean MES system, belong to steel pipe production technical field, including: unified identification and data acquisition module, for each steel billet / steel pipe is given unique identity code;Whole-process traceability management module, to unique identity code as index constructs "product digital twin archives";Intelligent scheduling and dynamic optimization module, for receiving production plan from ERP system generates process level scheduling instruction;Lean execution and visual monitoring module, for generating electronic job instruction according to process level scheduling instruction;System integration and closed-loop feedback module, for storing process level scheduling instruction and product quality in system knowledge base during production process, realize the continuous self-learning of system.The present application realizes material, process, quality data whole-process association and accurate traceability by constructing unified product identification system.

The application relates to a cross-process quality real-time correction method for a multi-process manufacturing process, which comprises collecting process parameter data, quality detection data and process meta-information corresponding to each manufacturing process of a part, constructing a manufacturing process sequence dataset according to the process sequence, constructing a cross-process quality propagation model to extract a whole-process state vector representing the quality state of the whole part manufacturing process and a final quality prediction result, constructing and training a cross-process correction decision model to obtain an optimal compensation vector to correct the process parameters, realizing dynamic control and real-time correction of the part manufacturing equipment, and designing a fine-tuning strategy based on incremental learning and reinforcement learning to dynamically optimize the control and correction process in each control cycle. The application solves the technical bottlenecks in traditional quality control, such as the difficulty in describing the quality transmission relationship between processes, the lag of dynamic correction and the lack of self-adaptive ability of the model, and improves the overall quality stability and consistency in the complex part manufacturing scene.

The application relates to the technical field of intelligent manufacturing, and discloses a mold life cycle and product process quality information linkage management and control system, which is characterized in that: a mold multi-dimensional state sensing module is used for accurately identifying micro-damage of the mold; a process quality real-time acquisition module is used for millisecond-level synchronous capture of process-quality data; a data feature extraction module is used for high-dimensional feature extraction of heterogeneous data flow; a mold health state diagnosis module is used for state evaluation and trend prediction of the mold; a process parameter self-adaptive optimization module is used for ternary dynamic collaborative optimization of mold health-process parameter-quality targets; a quality defect linkage identification module is used for quantitative root cause analysis of defect contribution degree; a predictive maintenance decision module is responsible for intelligent decision-making of maintenance urgency classification and an optimal time window; and a production execution module is used for accurate issuing of decision-making instructions, so that all the modules and a production management module cooperatively construct the linkage management and control system of the mold full life cycle and product process quality full elements.

The invention discloses an enameled wire production process quality on-line control closed-loop management system, and relates to the technical field of intelligent manufacturing, the system collects original physical data in the enameled wire production process in real time by arranging a sensor network, and after data processing, a coating feature data set and a process state data set are obtained; performing process quality analysis according to the coating characteristic data set, generating coating quality evaluation, performing production efficiency analysis through the process state data set when the coating quality meets requirements, performing process coupling steady-state analysis in combination with a process quality analysis result, and generating coupling state evaluation; according to the method, production data and evaluation results of all batches are filed, behavior offset in the production process is traced, a causal path is extracted to generate a control offset event set, and self-adaptive strategy backtracking and process correction are carried out. According to the system, closed-loop control of the production process is realized, the production quality and efficiency are improved, the production fluctuation is reduced, and the intellectualization and self-adaptive capability of the production process are enhanced.

The invention relates to the technical field of production control, in particular to an intelligent control system and method for a water-based paint production line, and the system comprises a parameter setting unit which is used for setting process control parameters of the production line in response to the process parameter setting operation of a user for the water-based paint production line; the data acquisition unit is used for acquiring a plurality of preset process quality grades corresponding to the target water-based paint production line, a production line allocation resource quantity corresponding to the target water-based paint production line and a grade bearing production rate corresponding to each preset process quality grade; and a preset quality grade weight corresponding to each preset process quality grade. Through cooperative work of the parameter setting unit and the resource allocation unit, the system can dynamically adjust resource allocation according to the process quality grade weight and the bearing production rate proportion, and the flexibility ensures that a production line can efficiently utilize resources under different production conditions, so that the production efficiency is improved.

This invention discloses a quality control method for a fully automated peanut oil production line, relating to the field of process quality control technology. The method includes: collecting process operating parameters and quality-related parameters from the pressing, degumming, and blending processes, preprocessing and synchronizing them over time to generate a quality status parameter set; determining the deviation of process control parameters based on the root cause process and its corresponding process control parameters, combined with the current operating constraints of the production line, and generating a reverse control strategy for the root cause process; converting the reverse control strategy into control commands and issuing them to the corresponding process actuators to adjust the process control parameters, and generating a collaborative control effect verification report. This invention, by analyzing the changing trends of the quality status parameter set, generates feedforward quality risk prediction results, achieving proactive prediction of potential quality problems, avoiding sudden quality anomalies during production, and improving production stability and efficiency.

The application provides a TO-263 plastic package surface mount device post-cutting device for pipe installation, which comprises a material guiding plate, an inclination adjusting plate and a supporting plate which are sequentially stacked and fixed on a punching die bottom plate; the top of the material guiding plate is provided with a material guiding groove, the top of the inclination adjusting plate is provided with a supporting inclined surface, and the top of the supporting plate is provided with a limiting groove; the center lines of the material guiding groove, the supporting inclined surface and the limiting groove are on the same plane, and the center line of the material guiding groove is on the same plane with the center line of a die material guiding groove on the punching die bottom plate. The limiting groove for collecting the material pipe and the inclination adjusting plate are arranged, so that the die for different products can be quickly positioned, and the production speed, process quality control and other problems in the process are significantly improved.

The invention relates to the technical field of medicines, in particular to a process quality control method of semeglutide, which is characterized in that a compound 1 has serious solution instability and can be quickly degraded along with time, and in a subsequent synthesis process, it must be ensured that an intermediate which is prepared freshly or is confirmed to be qualified in purity is used, and high performance liquid chromatography process monitoring is introduced. The invention provides a high performance liquid chromatography real-time monitoring method matched with a compound 1 to accurately control the reaction process so as to effectively inhibit an impurity chain reaction caused by degradation of raw materials, and for the characteristics of instability and continuous decomposition of the compound 1 in a solution, the invention provides a high performance liquid chromatography real-time monitoring method matched with the compound 1. The raw material conversion rate and the generation conditions of key over-modified impurities 1 and 2 can be synchronously monitored in the reaction process, and the raw material peak area smaller than or equal to 2.0% and the specific impurity peak area smaller than or equal to 5.0% (or 2.0%) serve as scientific and accurate reaction termination criteria. According to the active process control strategy, accumulation of impurities is inhibited from the source, and the problems of inaccurate feeding and batch difference caused by degradation of intermediates are solved.

This invention relates to the field of additive manufacturing quality control and data traceability technology, specifically to an intelligent inspection system for 3D printed parts with full-process quality traceability. The system includes: a data acquisition module for real-time acquisition of all-dimensional process parameters of the target printed part; a first processing module for generating a trust crack index characterizing the integrity of the data traceability chain; a second processing module for determining a traceability risk index characterizing the degree to which the system deviates from the safety boundary based on the trust crack index and a preset upper limit for the trust crack index; a risk decision module for outputting intervention actions based on the traceability risk index and all-dimensional process parameters via a preset deep reinforcement learning model; and a closed-loop control module for executing the intervention actions to adaptively correct the printing process of the target printed part. This invention achieves proactive avoidance and intelligent management of traceability risks, significantly improving the system's adaptive control level and the foresight of its decisions.

The invention relates to the technical field of intelligent manufacturing, in particular to a visual production management and control system for a die-casting process, and the system comprises a process data monitoring module which collects the process operation data and environment monitoring data of the die-casting process in real time, and automatically recognizes abnormal parameters based on process specifications; the process state analysis module is used for performing multi-dimensional feature analysis on the abnormal parameters, classifying abnormal process modes through a mode recognition algorithm, calculating dispersion indexes to evaluate the stability of the modes, and adopting a differentiation processing strategy according to a stability evaluation result; the process quality management module is used for carrying out classified management on analysis results; and the process state visualization module is used for displaying the abnormal process mode and the risk level thereof through an interactive interface, and highlighting the high-risk mode by adopting a layered coloring and dynamic sorting algorithm. According to the invention, through intelligent identification and visual display of the process abnormal mode, the quality risk accurate management and control and efficiency improvement of the die-casting production process are realized.

The application discloses a quality health degree evaluation and cross-machine-group early warning method based on multi-process roll-by-roll detection and relates to the technical field of metallurgical process quality management and control.The application realizes roll-by-roll actual measurement on each roll of material in each key process, collects quality data with the finished product roll number as the main key, automatically evaluates the quality health degree, and automatically issues a quality early warning before the roll enters the next process, so that the downstream process can know the risk in advance and adjust the process in advance.The application realizes the change from "blind box type production" to "transparent and pre-known production", effectively reduces batch waste, improves the yield, reduces quality loss, and is suitable for various metallurgical roll continuous production lines.

This invention provides a production process quality evaluation method, a production process quality evaluation device, and a production process quality evaluation program that enable the identification of product types or model numbers that should be prioritized for improvement when managing the production of a wide variety of industrial parts. [Solution] The production process quality evaluation device 1, which executes a production process quality evaluation method, comprises a loss amount database 13b stored in storage 13 for storing the amount of loss incurred in the production of machine parts during a predetermined production period, and an evaluation means consisting of a CPU 11 and the like for evaluating the quality of the machine parts production process based on the amount of loss.

The invention discloses a processing quality control method, a process quality cause searching method and a related device, which are used for improving the yield of products. The method comprises the steps of obtaining parameter information corresponding to a product; performing feature processing on the parameter information to obtain processed parameter information; inputting the processed parameter information into a pre-trained correlation factor analysis model to obtain accuracy information output by the correlation factor analysis model; obtaining the ranking of important factors influencing the product quality based on the accuracy information; and processing the processing equipment or jig corresponding to the product based on the ranking of the important factors. In the application, the parameter information of the product and the pre-trained correlation factor analysis model are adopted to determine the ranking of the important factors influencing the product quality, and the jigs are processed according to the ranking of the important factors, so that the jigs with lower ranking can be locked, the jigs with lower ranking are prevented from participating in the processing process, and the processing efficiency is improved. And thus, the yield of products is ensured.

The invention relates to the technical field of intelligent wiredrawing of copper-clad aluminum welding strips, in particular to an intelligent wiredrawing method and system for a copper-clad aluminum welding strip based on model prediction. The method comprises the steps that multi-source technological parameters in the wire drawing process and multi-source quality parameters of a finished welding strip are obtained in real time, wherein the quality parameters at least comprise the thickness representing the geometric dimension and the resistance representing the electrical property; inputting the process parameters into a pre-trained process quality prediction model to obtain a quality prediction value; and when the predicted value deviates from the target range, generating a process parameter adjustment instruction based on a model prediction control algorithm so as to control the wire drawing equipment. The system comprises corresponding data acquisition, model prediction, intelligent control and execution modules. According to the method, through fusion of multi-source information and model prediction control, collaborative and prospective accurate control over the geometric dimension and the electrical performance of the copper-clad aluminum welding strip is achieved, quality control is converted into active prevention from passive inspection, and the product percent of pass, the production efficiency and the consistency are remarkably improved.

The application provides a valve seat efficient machining process and quality control method, relates to the technical field of quality control, and comprises the following steps: selecting a suitable wear-resistant and corrosion-resistant alloy blank, removing defects through ultrasonic flaw detection, and then performing quenching and tempering treatment; integrally rough machining and stress relieving the blank after the quenching and tempering treatment, standardizing the semi-finishing parameter, accurately machining the sealing surface after the standardization, and performing precision machining on the sealing surface; after the sealing surface is qualified, performing finishing, judging whether the cutting parameter is optimized, performing final inspection, and storing the qualified products in the warehouse and separately storing the unqualified products. The application can effectively reduce deformation through flaw detection screening, quenching and tempering, and stress relieving, can improve machining consistency and efficiency through standardization of the cutting parameter, can guarantee sealing performance through closed-loop repair of the sealing surface, can greatly improve the qualified rate through parameter optimization and whole-process quality control, and realizes efficient and stable machining of the valve seat.

This invention discloses a full-process production traceability method based on a single field of "plan number and volume number," relating to the field of industrial production process control and data management technology. This invention uses the "plan number and volume number" as the unique core connecting field throughout the entire process, spanning raw materials, each production process, quality inspection, finished products, and delivery. It relies on a wide table to achieve unified collection of all-dimensional production data. Each process uses this identifier to collect and record relevant information such as production, process, quality, equipment, and personnel, forming a unique and complete production history for each material. This invention achieves forward and reverse traceability of materials, enabling rapid identification of anomalies and responsible links. It has advantages such as strong versatility, high traceability efficiency, complete data links, low management costs, and suitability for implementation in industrial settings across various industries. It effectively solves the common industry problems of chaotic identification, data silos, and difficult traceability in traditional production management. This invention has been fully implemented in a real industrial ERP system and can operate stably in real production environments for a long time, possessing mature engineering value and industrial promotion capabilities.

The invention discloses a production process monitoring method and system based on intelligent manufacturing, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: obtaining production process monitoring data in an intelligent manufacturing production scene, including four types of data of equipment operation, process parameters, product quality detection and material circulation; according to the production process monitoring method and system based on intelligent manufacturing, for change characteristics and business requirements of four types of data, differential acquisition periods are determined through four types of mechanisms of equipment life analysis, process quality association, quality tracing aging and material production influence; a Pareto optimal period scheme is generated through multi-target optimization integration, the problem of rigidity of a fixed period in the prior art can be avoided, and for data which are frequently changed such as equipment vibration and process temperature, the acquisition period is automatically shortened to guarantee the monitoring precision; and for data with slow change such as material inventory and equipment static parameters, the acquisition period is automatically prolonged to reduce resource consumption.

The present invention relates to a technology for appropriately and easily controlling the flow of raw materials. The present invention provides an injection molding machine comprising: a mounting part having an input passage for guiding raw material coming from a hopper to an input hole of a barrel; wherein the mounting part includes an opening / closing device for opening / closing the input passage; and wherein a control device controls the operation of the opening / closing device so as to allow or block the input of the raw material toward the input hole. According to the present invention, there is an advantage in that the consistency of process quality can be ensured and operational efficiency improved by reducing dependency on workers.